QuIP-1

Welcome to QuIP –
a web accessible tool set designed to support
analysis, management, and exploration
of whole slide tissue images for cancer research. This is an NIH funded multi-site collaborative
effort between
Stony Brook University, Emory University, Oak Ridge National Labs, and Yale University. Click on
any of the
colored buttons to launch the associated tool.

Image and Results Viewer

You can view a collection of ~3200 TCGA images from Brain(LGG & GBM), Breast(BRCA), Lung(LUAD), and Pancreatic(PAAD) cancer whole slide images and segmentation results. You can view the images, nuclear segmentations, as well as aggreement amongst segmentation algorithms that are presented as overlaid heatmaps. Click on the magnifier icon to choose algorithm results and heatmaps. You may zoom in, zoom out, and pan the images. Mouse Click: Zoom in, Shift-Click: Zoom out.

Visual Feature Analytics

This suite of interactive tools work together to allow interrogation of multiple
parameters including: cancer
type, age, patient demographics, nuclear morphologic features and survival, gene expression, and the
interaction
that impact survival curve, allowing direct visual evaluation as well as computer extracted
information to
evaluate patient survival.

Clinical Data Query

An interactive dashboard to explore interrelations between patient
demographics,
pathologist-generated diagnostic keywords, and outcomes based on publicly-available TCGA
data. Through integration into FeatureExplorer and FeatureScape individual corresponding
images and results can be explored further.

About

This site hosts web accessible applications and tools designed to support analysis, management, and
exploration of whole slide tissue images for cancer research. The goals of the parent project are to
develop, deploy, and disseminate a suite of open source tools and integrated informatics platform
that will facilitate multi-scale, correlative analyses of high resolution whole slide tissue image
data, spatially mapped genetics and molecular data. The software and methods will enable cancer
researchers to assemble and visualize detailed, multi-scale descriptions of tissue morphologic
changes and to identify and analyze features across individuals and cohorts.

The current set of applications has been developed and supported by several frameworks and middleware
systems including:

caMicroscope – Digital pathology data management, visualization and analysis
platform. It also includes FeatureDB, a database based on NoSQL technologies for
management and query of segmentation results and features from whole slide tissue image
analysis.